Update app.py

app.py

@@ -3,13 +3,37 @@ import spaces
 import torch
 import numpy as np
 from PIL import Image
+
+# ============================================================
+# Fix: Monkey-patch transformers video_processing_auto bug
+# Latest transformers has a bug where VIDEO_PROCESSOR_MAPPING
+# is None, causing TypeError in video_processor_class_from_name
+# ============================================================
+try:
+    from transformers.models.auto import video_processing_auto
+    _original_func = video_processing_auto.video_processor_class_from_name
+
+    def _patched_video_processor_class_from_name(class_name):
+        try:
+            return _original_func(class_name)
+        except TypeError:
+            # VIDEO_PROCESSOR_MAPPING_NAMES is None in some transformers versions
+            return None
+
+    video_processing_auto.video_processor_class_from_name = _patched_video_processor_class_from_name
+    print("[PATCH] video_processor_class_from_name patched successfully")
+except Exception as e:
+    print(f"[PATCH] Could not patch video_processing_auto: {e}")
+
 from diffusers import LongCatImageEditPipeline
 
-# --- Load pipeline on CPU at init ---
+# --- Load pipeline on CPU at init time ---
+print("Loading LongCat-Image-Edit-Turbo pipeline...")
 pipe = LongCatImageEditPipeline.from_pretrained(
     "meituan-longcat/LongCat-Image-Edit-Turbo",
     torch_dtype=torch.bfloat16,
 )
+print("Pipeline loaded on CPU.")
 
 
 @spaces.GPU(duration=120)
@@ -23,15 +47,16 @@ def edit_image(
     randomize_seed=True,
 ):
     if input_image is None:
-        raise gr.Error("Please upload an image / 이미지를 업로드해주세요.")
+        raise gr.Error("이미지를 업로드해주세요 / Please upload an image.")
     if not prompt.strip():
-        raise gr.Error("Please enter an editing prompt / 편집 프롬프트를 입력해주세요.")
+        raise gr.Error("편집 프롬프트를 입력해주세요 / Please enter an editing prompt.")
 
     if randomize_seed:
-        seed = np.random.randint(0, 2**31)
+        seed = int(np.random.randint(0, 2**31))
 
-    # Move to GPU (ZeroGPU allocated)
-    pipe.to("cuda")
+    # Enable CPU offload — model (~29GB) exceeds A10G VRAM (24GB)
+    # Safe to call multiple times; ensures proper device mapping with ZeroGPU
+    pipe.enable_model_cpu_offload()
 
     img = Image.fromarray(input_image).convert("RGB")
 
@@ -45,7 +70,7 @@ def edit_image(
         generator=torch.Generator("cpu").manual_seed(int(seed)),
     ).images[0]
 
-    return result, seed
+    return result, int(seed)
 
 
 # ========== Gradio UI ==========